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M. Sdika

3 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author3

Across the 3 of 3 papers where every author was matched, so the position is known.

fields
  • cs.CV2
  • eess.IV1

identity via Semantic Scholar / OpenAlex

most citedAutomatic Segmentation and Location Learning of Neonatal Cerebral Ventricles in 3D Ultrasound Data Combining CNN and CPPN

16 citations · 16 across the 1 of their papers we have counts for

collaborators

3 papers

eess.IV2020★ 16 cited

Automatic Segmentation and Location Learning of Neonatal Cerebral Ventricles in 3D Ultrasound Data Combining CNN and CPPN

Matthieu Martin, Bruno Sciolla, Michaël Sdika +2

Preterm neonates are highly likely to suffer from ventriculomegaly, a dilation of the Cerebral Ventricular System (CVS). This condition can develop into life-threatening hydrocepha…

cs.CV2018

Magnetic Resonance Spectroscopy Quantification using Deep Learning

Nima Hatami, Michaël Sdika, Hélène Ratiney

Magnetic resonance spectroscopy (MRS) is an important technique in biomedical research and it has the unique capability to give a non-invasive access to the biochemical content (me…

cs.CV2018

Towards integrating spatial localization in convolutional neural networks for brain image segmentation

Pierre-Antoine Ganaye, Michaël Sdika, Hugues Benoit-Cattin

Semantic segmentation is an established while rapidly evolving field in medical imaging. In this paper we focus on the segmentation of brain Magnetic Resonance Images (MRI) into ce…

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